{"as_of":"2026-08-07T20:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:421e94eab3051c06c0d8855357243af862e1d31c0aaa15fec3088902b873542a","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:47:33.687505Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-14T21:19:29.134491Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.12016","last_updated":"2024-10-04T06:26:20Z","snapshot_observed_at":"2026-07-06T18:32:32.228906Z","submitted_at":"2024-06-17T18:33:44Z","title":"Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.12016","snapshot_observed_at":"2026-08-07T10:47:33.687505Z","title":"Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05413","last_updated":"2025-07-29T15:28:07Z","snapshot_observed_at":"2026-08-07T10:40:42.464223Z","submitted_at":"2025-06-04T19:07:45Z","title":"SmoothRot: Combining Channel-Wise Scaling and Rotation for Quantization-Friendly LLMs","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:33.687505Z"},"links":{"cited_paper":"/paper/2406.12016","citing_paper":"/paper/2506.05413"},"observation_digest":"sha256:bb256f2643e3c78bcc798dded467ee1e79b1dba8042f131aabd75077ca659d34","observation_id":"20d59e83-e6ab-4eaf-9d29-6d51fb6dc8e4","resolution":{"observed_at":"2026-08-07T10:47:33.687505Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.12016","last_updated":"2024-10-04T06:26:20Z","snapshot_observed_at":"2026-07-06T18:32:32.228906Z","submitted_at":"2024-06-17T18:33:44Z","title":"Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.12016","snapshot_observed_at":"2026-08-06T20:08:08.298473Z","title":"Prefixing attention sinks can mitigate activation outliers for large language model quantization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.03865","last_updated":"2025-08-16T11:42:58Z","snapshot_observed_at":"2026-08-06T19:58:31.345730Z","submitted_at":"2025-07-05T02:29:23Z","title":"OrthoRank: Token Selection via Sink Token Orthogonality for Efficient LLM inference","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-06T20:08:08.298473Z"},"links":{"cited_paper":"/paper/2406.12016","citing_paper":"/paper/2507.03865"},"observation_digest":"sha256:b8c6685629f7ce8c9e2d63cd5f4a264d1e0e3d5756474a9aed2c002263078a45","observation_id":"e2bb6b72-cc2a-47c0-9b84-e450ce09f459","resolution":{"observed_at":"2026-08-06T20:08:08.298473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.12016","last_updated":"2024-10-04T06:26:20Z","snapshot_observed_at":"2026-07-06T18:32:32.228906Z","submitted_at":"2024-06-17T18:33:44Z","title":"Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.12016","snapshot_observed_at":"2026-08-05T20:59:36.904918Z","title":"Prefixing attention sinks can mitigate activation outliers for large language model quantization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.09627","last_updated":"2025-08-13T08:59:04Z","snapshot_observed_at":"2026-08-06T15:53:16.127835Z","submitted_at":"2025-08-13T08:59:04Z","title":"Physics- and geometry-aware spatio-spectral graph neural operator for time-independent and time-dependent PDEs","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T20:59:36.904918Z"},"links":{"cited_paper":"/paper/2406.12016","citing_paper":"/paper/2508.09627"},"observation_digest":"sha256:a84914e3cd4162866acee7f056d09ab0a02501bd0dfba9aa794f1b57f1c59919","observation_id":"e24cac0b-f4f2-49dc-8d33-b784f73db151","resolution":{"observed_at":"2026-08-05T20:59:36.904918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.12016","last_updated":"2024-10-04T06:26:20Z","snapshot_observed_at":"2026-07-06T18:32:32.228906Z","submitted_at":"2024-06-17T18:33:44Z","title":"Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.12016","snapshot_observed_at":"2026-08-05T21:05:02.757268Z","title":"Prefixing attention sinks can mitigate activation outliers for large language model quantization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.09628","last_updated":"2025-08-13T08:59:13Z","snapshot_observed_at":"2026-08-06T15:53:16.388100Z","submitted_at":"2025-08-13T08:59:13Z","title":"Attention's forward pass and Frank-Wolfe","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T21:05:02.757268Z"},"links":{"cited_paper":"/paper/2406.12016","citing_paper":"/paper/2508.09628"},"observation_digest":"sha256:849d1e924ec194934028bd53c6d197575b3e1127e85775716bb463ec6545f0a7","observation_id":"494fae0f-631f-4625-b04d-5b7192da51d5","resolution":{"observed_at":"2026-08-05T21:05:02.757268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.12016","last_updated":"2024-10-04T06:26:20Z","snapshot_observed_at":"2026-07-06T18:32:32.228906Z","submitted_at":"2024-06-17T18:33:44Z","title":"Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization","version":2},"cited_work":{"arxiv_id":"2406.12016","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.12016","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Pre- fixing attention sinks can mitigate activation outliers for large language model quantization.arXiv preprint arXiv:2406.12016","venue":null,"work_id":"f65cde27-9036-4b5d-baf3-a1cffda35a71","year":null},"citing_paper":{"arxiv_id":"2605.08504","last_updated":"2026-05-12T18:33:07Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T21:37:27Z","title":"A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-12T02:29:20.796512Z"},"links":{"cited_paper":"/paper/2406.12016","citing_paper":"/paper/2605.08504"},"observation_digest":"sha256:b1cf0ab7f64e715a6442dc706cf5e42c384a8910830cb8ca1a90c3453153a868","observation_id":"0c60cd6f-b44c-42f4-b3d6-f47676773e1f","resolution":{"observed_at":"2026-05-12T07:36:42.363601Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.12016","last_updated":"2024-10-04T06:26:20Z","snapshot_observed_at":"2026-07-06T18:32:32.228906Z","submitted_at":"2024-06-17T18:33:44Z","title":"Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization","version":2},"cited_work":{"arxiv_id":"2406.12016","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.12016","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Pre- fixing attention sinks can mitigate activation outliers for large language model quantization.arXiv preprint arXiv:2406.12016","venue":null,"work_id":"f65cde27-9036-4b5d-baf3-a1cffda35a71","year":null},"citing_paper":{"arxiv_id":"2605.08504","last_updated":"2026-05-12T18:33:07Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T21:37:27Z","title":"A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-14T21:03:25.624300Z"},"links":{"cited_paper":"/paper/2406.12016","citing_paper":"/paper/2605.08504"},"observation_digest":"sha256:2a52e6856663afbd56a7d1231ddfcb5345a8b650e84e8f5664c6744331b15d59","observation_id":"2a0b47b8-e58f-4db5-8898-38a289ecc985","resolution":{"observed_at":"2026-05-14T21:19:29.136886Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2406.12016/citation-record","integrity":"/paper/2406.12016/integrity","json":"/paper/2406.12016/citation-record.json","paper":"/paper/2406.12016"},"outbound":[],"paper":{"arxiv_id":"2406.12016","last_updated":"2024-10-04T06:26:20Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T18:32:32.228906Z","submitted_at":"2024-06-17T18:33:44Z","title":"Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.12016."}